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ThinkneoAI/mcp-server

by Various

Dual-protocol gateway: 62 MCP tools + A2A Protocol agent (82/82 TCK intentional) in a single governed runtime

T

MCP

ThinkneoAI/mcp-server

Added 1 June 2026

#a2a-protocol #agent-to-agent #ai-governance #enterprise #finops #guardrails #mcp #model-context-protocol

Overview

ThinkneoAI/mcp-server is a dual-protocol gateway that exposes 62 MCP tools and an A2A Protocol agent within a single governed runtime. It is written in Python and designed for developers who need to bridge multiple agent communication standards.

Best for

Best for
Developers exploring multi-protocol agent systems who need a unified tool gateway

Use cases

  • Integrating MCP and A2A protocol tools in one runtime
  • Building governed multi-agent systems with standardized tool access
  • Testing or prototyping with 62 pre-built MCP tools

Notes

ThinkneoAI/mcp-server is a dual-protocol gateway that exposes 62 MCP tools and an A2A Protocol agent within a single governed runtime. It is written in Python and designed for developers who need to bridge multiple agent communication standards.

1 stars on GitHub. Last updated 2026-05-31. Licensed Apache-2.0.

Use cases

  • Integrating MCP and A2A protocol tools in one runtime
  • Building governed multi-agent systems with standardized tool access
  • Testing or prototyping with 62 pre-built MCP tools

Pros

  • Supports both MCP and A2A protocols in a single gateway
  • Provides a large set of 62 pre-built tools for rapid development
  • Governed runtime helps enforce access controls and consistency

Cons

  • Very early stage with only 1 GitHub star and minimal community adoption
  • Limited documentation and real-world usage examples
  • Python-only implementation may not suit all deployment environments

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports both MCP and A2A protocols in a single gateway
  • Provides a large set of 62 pre-built tools for rapid development
  • Governed runtime helps enforce access controls and consistency

Cons

  • Very early stage with only 1 GitHub star and minimal community adoption
  • Limited documentation and real-world usage examples
  • Python-only implementation may not suit all deployment environments
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